Parameter estimation of coupled water and energy balance models based on stationary constraints of surface states

Sun, Jian; Salvucci, Guido D.; Entekhabi, Dara; Farhadi, Leila

doi:10.1029/2010wr009293

Cited by 20 publications

(31 citation statements)

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“…From this view, soil moisture feeds back to itself via atmospheric precipitation through two physical segments [Seneviratne et al, 2010;Ferguson et al, 2012;Guillod et al, 2014]. In the terrestrial segment, soil moisture is a major source of water for evapotranspiration and plays a vital role in determining the surface flux partitioning [Dirmeyer, 2011;Sun et al, 2011]. In the atmospheric segment, the turbulent fluxes (both sensible heat flux and latent heat flux) impact convection and precipitation through altering Planetary Boundary Layer (PBL) dynamics.…”

Section: Introductionmentioning

confidence: 99%

Effects of explicit convection on global land‐atmosphere coupling in the superparameterized CAM

Sun

Pritchard

2016

J Adv Model Earth Syst

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Conventional global climate models are prone to producing unrealistic land‐atmosphere coupling signals. Cumulus and convection parameterizations are natural culprits but the effect of bypassing them with explicitly resolved convection on global land‐atmosphere coupling dynamics has not been explored systematically. We apply a suite of modern land‐atmosphere coupling diagnostics to isolate the effect of cloud Superparameterization in the Community Atmosphere Model (SPCAM) v3.5, focusing on both the terrestrial segment (i.e., soil moisture and surface turbulent fluxes interaction) and atmospheric segment (i.e., surface turbulent fluxes and precipitation interaction) in the water pathway of the land‐atmosphere feedback loop. At daily timescales, SPCAM produces stronger uncoupled terrestrial signals (negative sign) over tropical rainforests in wet seasons, reduces the terrestrial coupling strength in the Central Great Plain in American, and reverses the coupling sign (from negative to positive) over India in the boreal summer season—all favorable improvements relative to reanalysis‐forced land modeling. Analysis of the triggering feedback strength (TFS) and amplification feedback strength (AFS) shows that SPCAM favorably reproduces the observed geographic patterns of these indices over North America, with the probability of afternoon precipitation enhanced by high evaporative fraction along the eastern United States and Mexico, while conventional CAM does not capture this signal. We introduce a new diagnostic called the Planetary Boundary Layer (PBL) Feedback Strength (PFS), which reveals that SPCAM exhibits a tight connection between the responses of the lifting condensation level, the PBL height, and the rainfall triggering to surface turbulent fluxes; a triggering disconnect is found in CAM.

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Section: Introductionmentioning

confidence: 99%

Effects of explicit convection on global land‐atmosphere coupling in the superparameterized CAM

Sun

Pritchard

2016

J Adv Model Earth Syst

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“…In that model [Sun et al, 2011], there was a single root zone moisture storage, and the fluxes of evapotranspiration, drainage, runoff and capillary rise to the root zone were parameterized based on that single storage state.…”

Section: Adaptation Of Methods For Estimating Noah Lsm Parametersmentioning

confidence: 99%

“…[6] The basic idea presented here, by Salvucci [2001] and by Sun et al [2011] is as follows: Using conditional averaging, the relation between soil moisture and precipitation (P) can be studied in place of the relation between soil moisture and evapotranspiration (ET), and drainage plus runoff (D+RO, which we will here denote as Q). The benefit of doing this is that very few long term and/or spatially distributed data sets exist for evapotranspiration and drainage, but many exist for precipitation and soil moisture.…”

Section: Methodsmentioning

confidence: 99%

“…Such measurements are too scarce (even with the advent of the FLUXNET network [Baldocchi et al, 2001]) to use for estimating continuous maps of surface parameters. As by Sun et al [2011], the method was demonstrated at FLUXNET sites, using a land surface model similar to the Noah LSM [Ek et al, 2003], but simplified to a single soil layer. Here the tendency terms is calculated directly from the current version of the Noah LSM (public release version 2.7.1) ensuring that the resulting parameter estimates are compatible with the NOAA WRF model and are applicable to atmospheric models that employ the Noah LSM.…”

Section: Introductionmentioning

confidence: 99%

“…[3] In a recent paper [Sun et al, 2011], we have presented a method for estimating land surface model parameters by maximizing a measure [Salvucci, 2001] of stationarity of model predicted tendencies (the time derivative of land surface moisture and temperature states with time). The measure is formed by conditionally averaging the tendency term by the state, which Salvucci [2001] showed to be zero for stationary systems.…”

Section: Introductionmentioning

confidence: 99%

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An alternate and robust approach to calibration for the estimation of land surface model parameters based on remotely sensed observations

Salvucci

Entekhabi

2011

Geophys. Res. Lett.

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[1] Atmospheric models include land surface parameterizations of heat and moisture fluxes. Most parameterizations derive from early models of layered soil and vegetation canopy that account for liquid, vapor and heat diffusion, radiation processes, and surface turbulence. The number of parameters in these models ranges roughly from 10 to 50. Some parameters are known to vary over a small range and/or not significantly impact predictions. Others are highly influential (e.g., Leaf Area Index), but are currently well estimated from satellite data. Many parameters, however, are highly influential, vary over a large dynamic range, cannot be estimated from satellite data, and cannot be readily upscaled from in-situ measurements. For such parameters (e.g., maximum stomatal conductance and soil hydraulic conductivity), values are assigned based on look-up tables sorted by land cover and soil texture. Here we present a method for estimating these parameters by minimizing a measure of nonstationarity of model-predicted moisture state variable tendencies. This method has advantages over calibration: 1) it does not require flux data (e.g., evapotranspiration); and 2) the tendency terms are evaluated at the model grid and thus yield parameters that are effective for that scale. The method is demonstrated with the Noah Land Surface Model, using remotely-sensed soil moisture, at a site in California. Preliminary results indicate that the method is robust and performs better than both: 1) calibration to soil moisture observations, which can lead to large, compensating errors in drainage and evaporation; and 2) minimizing the sum of squares of innovations of soil moisture updates. Citation: Salvucci, G. D., and D. Entekhabi (2011), An alternate and robust approach to calibration for the estimation of land surface model parameters based on remotely sensed observations, Geophys. Res. Lett., 38, L16404,

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Estimation of land surface water and energy balance parameters using conditional sampling of surface states

et al. 2014

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Numerical models of heat and moisture diffusion in the soil-vegetation-atmosphere continuum are linked through the moisture flux from the surface to the atmosphere. This mass flux represents a heat exchange as latent heat flux, coupling water, and energy balance equations. In this paper, a new approach for estimating key parameters governing moisture and heat diffusion equation and the closure function which links these equations, is introduced. Parameters of the system are estimated by developing objective functions that link atmospheric forcing, surface states, and unknown parameters. This approach is based on conditional averaging of heat and moisture diffusion equations on land surface temperature and moisture states, respectively. A single objective function is expressed that measures moisture and temperaturedependent errors solely in terms of observed forcings and surface states. This objective function is minimized with respect to the parameters to identify evaporation and drainage models and estimate water and energy balance flux components. The approach is calibration free (surface flux observations are not required), it is not hampered by missing data and does not require continuous records. Uncertainty of parameter estimates is obtained from the inverse of Hessian of the objective function, which is an approximation of the error covariance matrix. Uncertainty analysis and analysis of the covariance approximation, guides the formulation of a well-posed estimation problem. Accuracy of this method is examined through its application over three different field sites. This approach can be applied to diverse climates and land surface conditions with different spatial scales, using remotely sensed measurements.

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Parameter estimation of coupled water and energy balance models based on stationary constraints of surface states

Cited by 20 publications

References 80 publications

Effects of explicit convection on global land‐atmosphere coupling in the superparameterized CAM

Effects of explicit convection on global land‐atmosphere coupling in the superparameterized CAM

An alternate and robust approach to calibration for the estimation of land surface model parameters based on remotely sensed observations

Estimation of land surface water and energy balance parameters using conditional sampling of surface states

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